The controversial Project Maven received a 580% funding increase in this year's bill. As AI and machine learning algorithms are integrated into defense tech, spending is only going to increase in years to come.

Recent work on adversarial attack has shown that Projected Gradient Descent (PGD) Adversary is a universal first-order adversary, and the classifier adversarially trained by PGD is robust against a wide range of first-order attacks. However, it is worth noting that the objective of an attacking/defense model relies on a data distribution, typically in the form of risk maximization/minimization: $\max\!/\!\min \mathbb{E}_{p(\mathbf{x})} \mathcal{L}(\mathbf{x})$, with $p(\mathbf{x})$ the data distribution and $\mathcal{L}(\cdot)$ a loss function. While PGD generates attack samples independently for each data point, the procedure does not necessary lead to good generalization in terms of risk maximization. In the paper, we achieve the goal by proposing distributionally adversarial attack (DAA), a framework to solve an optimal {\em adversarial data distribution}, a perturbed distribution that is close to the original data distribution but increases the generalization risk maximally. Algorithmically, DAA performs optimization on the space of probability measures, which introduces direct dependency between all data points when generating adversarial samples. DAA is evaluated by attacking state-of-the-art defense models, including the adversarially trained models provided by MadryLab. Notably, DAA outperforms all the attack algorithms listed in MadryLab's white-box leaderboard, reducing the accuracy of their secret MNIST model to $88.79\%$ (with $l_\infty$ perturbations of $\epsilon = 0.3$) and the accuracy of their secret CIFAR model to $44.73\%$ (with $l_\infty$ perturbations of $\epsilon = 8.0$). Code for the experiments is released on https://github.com/tianzheng4/Distributionally-Adversarial-Attack

Accurate time-series forecasting is vital for numerous areas of application such as transportation, energy, finance, economics, etc. However, while modern techniques are able to explore large sets of temporal data to build forecasting models, they typically neglect valuable information that is often available under the form of unstructured text. Although this data is in a radically different format, it often contains contextual explanations for many of the patterns that are observed in the temporal data. In this paper, we propose two deep learning architectures that leverage word embeddings, convolutional layers and attention mechanisms for combining text information with time-series data. We apply these approaches for the problem of taxi demand forecasting in event areas. Using publicly available taxi data from New York, we empirically show that by fusing these two complementary cross-modal sources of information, the proposed models are able to significantly reduce the error in the forecasts. Keywords: Deep learning, Data fusion, Cross modality learning, Time series forecasting, Textual data, Taxi demand, Special events, Urban mobility 1. Introduction Understanding what drives the travel behavior of people is a key research topic for developing effective and efficient intelligent transportation systems that adapt to the travel demand. However, typical approaches focus only on capturing recurrent mobility trends that relate to habitual/routine behaviour [1], and on exploiting short-term correlations with recent observation patterns [2, 3]. While this type of approaches can be successful for long-term planning applications or for modeling demand in non-eventful areas such as residential neighborhoods, in lively and highly dynamic areas that are prone to the occurrence of multiple special events, such as music concerts, sports games, festivals, parades and protests, these approaches fail to accurately model mobility demand [4]. As we move towards the deployment of autonomous vehicles, understanding and being able to anticipate mobility demand becomes crucial, especially in shared-mobility scenarios, as this allows for properly managing fleets and increasing user-satisfaction. In order to capture the effects of events, one can exploit the vast amount of information that is shared online about what is planned to take place in the city. However, most of this information is typically in the form of unstructured natural-language text.

Exponential advancements in technology within the last half century have profoundly reshaped humanity and continue to do so continuously. Concepts which once seemed as fantasy Sci-Fi, visualized through Hollywood hits such as The Terminator (1984) and Eagle Eye (2008) have steadily and inconspicuously become a part of our reality. More recently, the futurist show, Black Mirror (2011) featured on Netflix gives us a glimpse of what the future may hold. One thing in common for all of these shows is the portrayal of possibilities with regards to advancements in computer technology -- be it in the form of a highly intelligent, autonomous, sophisticated robot like the Terminator (with a massive capacity for destruction)or ARIIA, a supercomputer able to manipulate almost all connected devices and command its victims to fulfill its agenda. Artificial Intelligence (AI) is seen to be the core driver of current trends within the tech sector and has vastly developed since the term was first coined in the 1950's. It is embedded in our phones, in the form of online chat bots and as phone operators to name a few contemporary use cases.

The stern of a US destroyer that was blown off the ship by a Japanese mine 75 years ago, killing 71, has been found off Alaska. The fragment of the USS Abner Read was found in the Bering Sea off the Aleutian island of Kiska, where it sank after being torn off by an explosion while conducting an anti-submarine patrol. The remaining crew managed to save the ship, which was repaired after the attack. On July 17, a NOAA-funded team of scientists from Scripps Institution of Oceanography at the University of California San Diego and the University of Delaware discovered the missing 75- foot stern section in 290 feet of water off of Kiska, one of only two United States territories to be occupied by foreign forces in the last 200 years. After sonar mounted to the side of the research ship Norseman II identified a promising target, the team sent down a deep-diving, remotely operated vehicle to capture live video for confirmation.

Oracle, a company not exactly known for having the best relationship with the open source community, is releasing a new open source tool today called Graphpipe, which is designed to simplify and standardize the deployment of machine learning models. The tool consists of a set of libraries and tools for following the standard. Vish Abrams, whose background includes helping develop OpenStack at NASA and later helping launch Nebula, an OpenStack startup in 2011, is leading the project. He says as his team dug into the machine learning workflow, they found a gap. While teams spend lots of energy developing a machine learning model, it's hard to actually deploy the model for customers to use.

If 2017 was the year artificial intelligence rose to prominence, 2018 is when we're seeing it go mainstream. Whichever area you work in, it's likely AI will become increasingly prevalent in your everyday activity. Wherever you are in the world – whether you are an expert in AI, someone whose job increasingly uses AI or simply an interested reader we would like to hear from you. Earlier this year, the Guardian published a long read that asked: Has technology evolved beyond our control? Its author, James Bridle, argued that "our technologies are extensions of ourselves, codified in machines and infrastructures, in frameworks of knowledge and action. Computers are not here to give us all the answers, but to allow us to put new questions, in new ways, to the universe."

As with other social institutions, marriage has always evolved alongside changes in technology. Electric appliances gave rise to wives pursuing paid work outside the patriarchal burdens of homemaking. The latex condom and other birth control tech offered both partners more choices and provoked society to reckon with the idea that women, too, seek sexual gratification from their relationships. Now, argues economist Marina Adshade, another technology seems poised to radically transform this age-old practice: sex robots. She explains how titillating androids of the future might disentangle our association between sexual intimacy and marriage--and, in doing so, remake our matrimonial unions for the better.

For millions of people suffering from diabetes, new technology enabled by artificial intelligence promises to make management much easier. Medtronic's Guardian Connect system promises to alert users 10 to 60 minutes before they hit high or low blood sugar level thresholds, thanks to IBM Watson, "the same supercomputer technology that can predict global weather patterns." Startup Beta Bionics goes even further: In May, it received Food and Drug Administration approval to start clinical trials on what it calls a "bionic pancreas system" powered by artificial intelligence, capable of "automatically and autonomously managing blood sugar levels 24/7." An artificial pancreas powered by artificial intelligence represents a huge step forward for the treatment of diabetes--but getting it right will be hard. Artificial intelligence (also known in various iterations as deep learning and machine learning) promises to automatically learn from patterns in medical data to help us do everything from managing diabetes to finding tumors in an MRI to predicting how long patients will live.

Let's start by dispelling the most common misconception: There is very little if any true artificial intelligence (AI) being incorporated within enterprise security software. The fact that the term comes up frequently is largely to do with marketing, and very little to do with the technology. Pure AI is about reproducing cognitive abilities. That said, machine learning (ML), one of many subsets of artificial intelligence, is being baked into some security software. But even the term machine learning may be employed somewhat optimistically.